Kim SY, et al. Yonsei Med J. 2022 Jan;63(Suppl):S56-S62. https://doi.org/10.3349/ymj.2022.63.S56

Jang J, et al. J Korean Med Sci. 2018 Mar;33(15):e116. https://doi.org/10.3346/jkms.2018.33.e116

To identify population-based cancer indicators and construct monitoring systems for the entire lifecycle of cancer patients using a modified Delphi method. A modified Delphi method was used to identify the cancer indicators and measurement by scoping review and gray literature. The final list of cancer indicators was developed by consensus of 11 multidisciplinary experts over multiple rounds and rating scored the importance of each indicator on a 10-point scale. Frequency analysis was performed to rate with median scores ≥7 and finalized the list of indicators according to the priority. Initially, 254 indicators were identified, of which 94 were considered important and feasible. After two rounds of rating by the experts and panel discussions, 26 indicators were finalized in six domains: primary prevention (n = 7), secondary prevention (n = 11), treatment (n = 2), quality of life (n = 4), survivor management (n = 1), and end-of-life care (n = 1). The Donabedian model used for examining health services and the Institute of Medicine quality of healthcare domains were applied to the measurement system. Panel experts identified cancer indicators based on priorities with a high level of consensus, providing a scrupulous foundation for community-based monitoring of cancer patients.

Background: The long-term economic impact of frailty measured at the beginning of elderhood is unknown. Objective: The objective of our study was to examine the association between an individual’s frailty index at 66 years of age and their health care costs and utilization over 10 years. Methods: This retrospective cohort study included 215,887 Koreans who participated in the National Screening Program for Transitional Ages at 66 years of age between 2007‐2009. Frailty was categorized using a 39-item deficit accumulation frailty index: robust (<0.15), prefrail (0.15‐0.24), and frail (≥0.25). The primary outcome was total health care cost, while the secondary outcomes were inpatient and outpatient health care costs, inpatient days, and number of outpatient visits. Generalized estimating equations with a gamma distribution and identity link function were used to investigate the association between the frailty index and health care costs and utilization until December 31, 2019. Results: The study population included 53.3% (n=115,113) women, 32.9% (n=71,082) with prefrailty, and 9.7% (n=21,010) with frailty. The frailty level at 66 years of age was associated with higher cumulative total costs (robust to frail: $19,815 to $28.281; P<.001), inpatient costs (US $11,189 to US $16,627; P<.001), and outpatient costs (US $8,625 to US $11,654; P<.001) over the next 10 years. In the robust group, a one-year increase in age was associated with increased total health care costs (mean change per beneficiary per year: US $206.2; SE: $1.2; P<.001), inpatient costs (US $126.8; SE: $1.0; P<.001), and outpatient costs (US $74.4; SE: $0.4; P<.001). In the frail group, the increase in total health care costs was greater compared to the robust group (difference in mean cost per beneficiary per year: US $120.9; SE: $5.3; P<.001), inpatient costs (US $102.8; SE: $5.22; P<.001), and outpatient costs (US $15.6; SE: $1.5; P<.001). Similar results were observed for health care utilization (P<.001). Among the robust group, a one-year increase in age was associated with increased inpatient days (mean change per beneficiary per year: 0.9 d; P<.001) and outpatient visits (2.1 visits; P<.001). In the frail group, inpatient days increased annually compared to the robust group (difference in the mean inpatient days per beneficiary per year: 1.5 d; P<.001), while outpatient visits increased to a lesser extent (difference in the mean outpatient visits per beneficiary per year: −0.2 visits; P<.001). Conclusions: Our study demonstrates the potential utility of assessing frailty at 66 years of age in identifying older adults who are more likely to incur high health care costs and utilize health care services over the subsequent 10 years. The long-term high health care costs and utilization associated with frailty and prefrailty warrants public health strategies to prevent and manage frailty in aging populations.

『국제개발협력』은 공적개발원조(Official Development Assistance, ODA) 관련 이슈 뿐만 아니라 국제개발협력과 관련한 모든 제반문제에 대한 이론과 정책대안을 모색하고 관련 자료를 제공하는데 그 목적이 있다. 논문의 성격 및 형태는 순수이론적 논문, 실증분석논문, 사례연구, 응용연구 및 정책분석 등 방법론상의 제약을 두지 않는 순수학술논문으로 한다.

Background: The COVID-19 pandemic accelerated global telehealth adoption, prompting the South Korean government to temporarily legalize telemedicine in 2020 and subsequently launch a pilot program in 2023. As South Korea transitions to a postpandemic digital health environment, understanding the factors associated with willingness to use (WTU) and willingness to pay (WTP) for telemedicine and teleconsultation is essential for informing effective policy and service design. However, few studies have explored how preferences vary across clinical domains or user groups. Objective: This study examined the factors that influence WTU and WTP for telemedicine and teleconsultation across 5 clinical domains: dermatological, psychiatric, musculoskeletal, internal medicine, and cancer disorders. Methods: A cross-sectional survey was conducted among 552 participants aged 19-69 years in South Korea, selected through stratified sampling. Multiple logistic regression analysis was used to examine WTU and WTP, considering sociodemographic factors and previous telemedicine experience. Results: Participants’ age, residence, and previous telemedicine experience significantly influenced their WTU and WTP for telemedicine services. WTP increased with age for both telemedicine (P-for-trend=.02) and teleconsultation (P-for-trend=.001). Noncapital residents showed significantly higher WTU for teleconsultation than capital area residents (odds ratio [OR] 1.48, 90% CI 1.03-2.12; P=.07). Participants with previous telemedicine experience showed higher WTU for telemedicine (OR 4.07, 90% CI 1.84-9.04; P=.004) and teleconsultation (OR 2.21, 90% CI 1.21-4.06; P=.03), and higher WTP for telemedicine (OR 2.89, 90% CI 1.84-4.54; P<.001) and teleconsultation (OR 2.76, 90% CI 1.77-4.30; P<.001). WTU and WTP varied by clinical domain: psychiatric care showed the highest WTU (64.5%) and WTP (27.0%) for telemedicine, while cancer disorders showed higher WTU (48.6%) and WTP (24.8%) for teleconsultation than for telemedicine. Conclusions: WTU and WTP for telemedicine and teleconsultation differ substantially depending on service type, clinical domain, and user characteristics. These findings highlight the importance of considering prior telemedicine experience, regional access disparities, and condition-specific care needs when designing digital health strategies. Accordingly, flexible, user-centered telehealth policies are needed to support service accessibility and equitable implementation in the post–COVID-19 era. The insights from this study can serve as a practical foundation for developing inclusive digital health systems in countries undergoing similar transitions.